Sentiment Analysis, or opinion mining, is a Natural Language Processing(NLP) technique that classifies data as expressing positive, negative, or neutral feelings. The method is quite popular for analyzing customer sentiments, social media feedback, product reviews, and text-based content related to people’s moods.
Sentiment Analysis looks at text structure and identifies the distinct emotional features. For example, for the customer who reviews the product as “The product is awesome! I love it,” a Sentiment Analyzer would record that as a positive sentiment. On the contrary, something like “I am let down; the product was not what I thought it would be” would receive a negative tag.
Sentiment Analysis with Default Sentiment Analyzer
Default Sentiment Analyzers Overview
Pre-trained models, known as default sentiment analyzers, are often used to make sentiment analysis tasks easier and faster. NLTK, TextBlob, or VADER libraries already provide these grammars and models. These tools are suitable for a broader range of functions and are relatively easy to implement for fast prototyping or in case someone needs a general analysis of the opinions.
These are some of the default sentiment analyzers that are popular:
NLTK’s VADER Sentiment Analyzer: It best analyzes sentiment in short social media text posts.
TextBlob: It is built for general sentiment and provides a fundamental analysis of interconnected text content for sentiment scoring.
SpaCy: This is a natural language processing library, which is a little tricky, but it possesses the critical function of allowing building sentiment capabilities on previously trained language models.
Contribution and Utility of Sentiment Analysis
The contribution of Sentiment Analysis reaches numerous domains:
- Business Intelligence: Product and service enhancement through customer feedback analysis.
- Social Media Monitoring: Negative and hurtful posts on social media platforms are dealt with.
- Brand Management: User’s attraction or dislike of a company’s product on various forums.
- Customer Support: With knowledge of customers feeling, it is easier to attend to customer service.
- Through these applications, organizations can achieve meaningful analysis of the reasoning behind their targeted audiences’ attitudes toward rational marketing.
Instances and Contexts of Application of Default Sentiment Analyzers
Now, let’s take a few cases to clarify what is a Default Sentiment Analyzer:
Example 1: VADER for Social Media Posts
Let’s say you are trying to figure out what the general public feels about a particular movie using Twitter right after its release while trying to gauge their overall sentiment.
Text: “It definitely lived up to the expectations!”
Analyzer: VADER (Valence Aware Dictionary and sentiment Reasoner) is ideal for analyzing social media texts, underscoring users’ reliance on punctuation, capitalization, and emojis.
Analysis: In all possibilities, words like “incredible” and “top-notch” would make VADER gauge this event positively with a high score for positivity.
Example 2: Customer Review Sentiment Using TextBlob
Assume a client review which focuses on an online retail product:
Analyzer: TextBlob is generally cited for analyzing text comments via emphatic scale and assignation of subjectivity.
Analysis: TextBlob would evaluate this review as unfavorable as it contains words such as “took forever” and “Not happy on the whole,” which show negation.
Example 3: News Sentiment Analysis Using NLTK
Assume that one is analyzing news headlines:
Text: “An unlooked-for fall in the frugality is observed while affectation has constantly been on the upward trend.”
Analyzer: NLTK (National Language Toolkit) has numerous options for sentiment analysis but is not optimized for social or conversational frameworks.
Analysis: According to NLTK’s explanation, it would most probably evaluate this as unfavorable when such phrases as “unbearable fall” and “inflation has remained on the growth trend” are still used.
Installing the Libraries that Perform Sentiment Analysis
Pip install nltk text blob vaderSentiment spacy.
For SpaCy, it is essential to have a worked-out language model for sentiment analysis. Such as:
Python -m spacy download en_core_web_sm
Using a Default Sentiment Analyzer
Detailed Instructions on How to Perform Sentiment Analysis Automatically
In our case, we will perform sentiment analysis in Python with the help of three libraries: TextBlob, VADER, and NLTK. All examples will be provided with the relevant sample code.
Step 1: Make the Necessary Preparations
Make sure that you have the following libraries in your Python environment:
Pip install nltk text blob vaderSentiment
Let us engage in an insightful exploration of how to perform sentiment analysis and clarify the objectives of the tasks ahead – analysis of sentiment and its measurement in some given examples.
Example 1: Text Blob to View Product Reviews
#Scenario: Imagine you are in charge of a product on an online store and intend to read comments and try to understand the sentiments expressed about the product.
From text blob import TextBlob
review = "The phone is amazing! It has a fantastic camera and lasts all day, I could not be happier with my purchase."
blob = TextBlob(review)
# Extracting polarity and subjectivity
polarity = blob.sentiment.polarity
subjectivity = blob.sentiment.subjectivity
print(f"Polarity: {polarity}, Subjectivity: {subjectivity}")
#Output Interpretation: Polarity: 0.5750000000000001, Subjectivity: 0.9
#Subjectivity: Scores close to 1 indicate the review is based on personal opinion rather than fact.
#Explanation: This review will be expected to have a high polarity score because of the sentiments catered in the review, like the use of words such as "amazing,” “fantastic,” and "happier," the customer reflects their satisfaction.
Example 2: Social Media Sentiment Analysis with VADER
#Scenario: Imagine you are a social media manager tracking user sentiment for a new marketing campaign on Twitter.
#Text: "Wow, the new ad campaign is stunning! I love how creative it is. #AmazingAd #MarketingWin"
#Code Implementation:
From vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
analyzer = SentimentIntensityAnalyzer()
tweet = "Wow, I love how creative it is. #AmazingAd #MarketingWin"
scores = analyzer.polarity_scores(tweet)
print(f"Sentiment Scores: {scores}")
#Compound Score: Scores above 0.05 indicate positive sentiment, scores below -0.05 indicate negative sentiment and scores close to 0 indicate neutral sentiment.
#Explanation: Words like "stunning" and "love" would boost the positive sentiment score. Hashtags like #AmazingAd and #MarketingWin also increase positivity as they convey excitement.
Example 3: Sentiment-Based Customer Service Ticket Classification Using NLTK
#Scenario: A customer support team’s sentiment-based matrix addresses negative sentiment tickets first.
#Text:: “I’m so disappointed with the last update of the software. It’s bringing up so numerous problems.”
#Code Implementation:
Import nltk
from nltk. Sentiment import SentimentIntensityAnalyzer
nltk.download('vader_lexicon')
analyzer = SentimentIntensityAnalyzer()
ticket = "It is throwing so many issues."
scores = analyzer.polarity_scores(ticket)
print(f"Sentiment Scores: {scores}")
#Output Interpretation:
[nltk_data] Downloading package vader_lexicon to
[nltk_data] C:\Users\prasa\AppData\Roaming\nltk_data...
#Negative Score: A high negative and low positive score indicates a primarily negative sentiment.
#Explanation: The analyzer would report negativity because of the sentiments 'unhappy' and phrases that suggest 'causing issues.' This ticket should be given preference for quicker reply.
Improving Sentiment Analysis Results
Default sentiment analyzers serve their purpose. However, many improvements are possible, especially where the context and subtext are not straightforward.
Issues Surrounding Sarcastic Statements
Sarcastic statements may often be misconstrued, especially with default analyzers based on the positive-negative scale. Consider this statement.
Text: “Oh, wonderful. Yet another software update that messes everything up.”
The feeling is a negative impression, even in the face of the word” excellent,” which is present in such a statement. Default sentiment analyzers will see “wonderful” as a positive value without appreciating the sarcasm.
Solution: one way to do this is to use automatically trained sentiment models that incorporate aspects of contextual embedding to simplify expression tone and specific word combinations associated with sarcasm.
Performing a Textual Analysis Over a Non-Generic Sociology
A custom lexicon template helps enhance the understanding of alien tongues, making the relative heartings more axial than the manual norm. For example, in the healthcare sector, unique terminologies such as ‘critical’ and ‘positive’ may not generally be positive but have a particular usage context.
For example:
In medicine, test results rated “positive” could harm the patient.
In finance, a specific term that is used is very bearish. If this term is analyzed by general sentiment, then there is no such notion that general sentiments can easily be understood.
Utilizing supervised methods and trained labeled data helps achieve accurate models; these include defined approaches that help provide better analysis of large or industry-specific datasets.
Improving the Standard Models to Fit the Context
Adapting supervised methods and trained labeled data helps achieve accurate models; these include defined approaches that allow better analysis of large or industry-specific datasets.
Fine-tuning Example:
Gather a dataset related to the end goal (e.g., healthcare reviews and finance articles).
Create a clean dataset and tag each value with sentiment.
Train using transfer learning with a model like BERT or a deep learning-based model explicitly designed for sentiment classification and trained with the ever-increasing number of custom-labeled data.
Note:-
If you get any errors, please try to run these commands
conda update numpy
conda update h5py
conda install -c conda-forge spacy
python -m spacy download en_core_web_sm
!pip install sci-kit-learn
conda install -c anaconda scikit-learn
Conclusion
Using off-the-shelf sentiment analysis tools is one of the practical ways of performing the sentiment classification of textual data in different domains. NLTK, TextBlob, and VADER are libraries that make the task easier and are suitable for preliminary studies. Not to be omitted, however, as we have seen, default analyzers aren’t without their failures, in some cases even rendering the context meaningless due to the absence of sarcasm, usage of particular jargon, or advanced text.
For better performance in sentiment analysis, especially for Niche areas, it is advisable to use:
Shifting lexicon style
Combining lexicon with machine learning model-based style
Modifying the models to be able to address the area of focus based on already existing datasets
In short, models employed in either tools or trained specifically for opinion mining bring tremendous value to business and research that explores the patterns in public perception or client views. The method of analysis to be employed determines the nature of the data and the specifics of the analysis.
